Abstract
This paper presents a new iterative algorithm to compute a conjectured supply function electricity market equilibrium with DC transmission network constraints. This approach extends, to a network constrained system, a model previously developed by the authors for the single-bus case. At each iteration nodal prices are used to split the market into single prices areas. Since each area can be treated as a single-bus market from the transmission constraints' point of view, the single-bus algorithm is applied to compute the generators supply functions for each area. These new generators strategies are then cleared to determine new nodal prices and areas for the next iteration, and convergence is achieved when the network lines status and strategies of the generators do not change significantly in two consecutive iterations. The current approach has also been extended to deal with nodal elastic demands. Unlike previous approaches, the main contribution of this work is that the parameters of the first order approximation of the conjectured supply functions (intercepts and slopes) are endogenously determined, coherently with the network lines status. The algorithm has been applied to some illustrative case examples, and to a simplified version of the MIBEL market (Spain-Portugal). Results have shown to be very close to real data, and very relevant to analyze the economic impact of the capacity network constraints.

Abstract
Electricity physical and financial contracts are common instruments used by generators to reallocate their risk among other market participants. Likewise, from the regulator point of view, contracts are understood and promoted as a way to mitigate the generators market power exercise. Therefore the analysis of the impact of these derivatives on the electricity market prices and on the agents' strategic behavior is of interest for both regulator and market participants. This paper focuses on how physical and financial electricity contracts affect the generators' strategies, but does not deal with determining contracts' quantities or prices, that are assumed to be fixed and known. Strategies are computed for selected agents and different physical or financial contracted quantities, assuming inelastic demand. A CSFE methodology for endogenous conjectures computation, developed by the authors in previous works, has been applied. The examples highlight the importance of computing the conjectures when the contracted quantities vary, since the variable agents' position can significantly change the market results. The main conclusion is that the common understanding that contracts can decrease the market prices may be called into question. © 2012 IEEE.

Abstract
This paper proposes a methodology for determining the optimal bidding strategy of a retailer who supplies electricity to end-users in the short-term electricity market. The aim is to minimize the cost of purchasing energy in the sequence of trading opportunities that provide the day-ahead and intraday markets. Agenetic algorithm has been designed to optimize the parameters that define the best purchasing strategy. The proposed methodology has been tested using real data from the Spanish day-ahead and intraday markets over a period of two years with a significant cost reduction with respect to trading solely in the day-ahead market.

Abstract
This paper proposes an optimization model based on a centralized dispatch to analyze the impact of plug-in electric vehicles (PEV) penetration on the Spanish electricity demand and price. Different charging strategies, from the plug-and-charge strategy to the smartest vehicle-to-grid (V2G) capabilities, have been simulated and compared with the non PEV scenario, under different penetration values. Traditional generation resources have been represented with technical aspects such as ramps and minimum loads. Hydraulic resources management is also partially supported but limited to weekly management. Distribution network constraints and how they could limit the contribution of PEV to the grid services have been neglected. The model considers both load and reserve requirements to evaluate the PEV contribution to energy production or consumption, but also to secondary reserve regulation. Interesting results comparing the different simulation scenarios are discussed in the case examples. © 2012 IEEE.

Abstract
Modeling competition in the future European Electricity Market (EEM) suggests considering new perspectives of leadership between the participant generators. EEM models should then be able to represent the possible existence of leaders and followers, and Conjectural Stackelberg equilibriums could be a powerful approach to do so. These asymmetric equilibriums can represent several types of competitive advantages among the market participants, though existing literature approaches generally assume single-leader-follower games. In addition, the complexity of the resolution methodologies sometimes compromises their applicability, especially if large-scale problems have to be solved. In this paper, a multi-leader-follower conjectural Stackelberg equilibrium model is presented. A simple convex quadratic optimization problem is proposed for its resolution, and the conditions for the equilibrium existence are discussed. A case study is presented to validate the model, and to analyze how market competition changes with the number of leaders or followers. The main conclusion is that if leadership spreads and Stackelberg models are used for its representation, regulatory policies should be oriented to increase the number of followers. However, there exists a maximum number of followers for which Stackelberg equilibriums does not exist, suggesting a deeper analysis for these cases to understand that matter and to decide the possible use of other market models. © 2012 IEEE.

Abstract
Electric drive vehicle technologies are being considered as possible solutions to mitigate environmental problems and fossil fuels dependence. Several studies have used life cycle analysis technique, to assess energy use and CO2 emissions, addressing fuels Well-to-Wheel life cycle or vehicle's materials Cradle-to-Grave. However, none has considered the required infrastructures for fuel supply. This study presents a methodology to evaluate energy use and CO2 emissions from construction, maintenance and decommissioning of support infrastructures for electricity and fossil fuel supply of vehicles applied to Portugal case study. Using Global Warming Potential and Cumulative Energy Demand, three light-duty vehicle technologies were considered: Gasoline, Diesel and Electric. For fossil fuels, the extraction well, platform, refinery and refuelling stations were considered. For the Electric Vehicle, the Portuguese 2010 electric mix, grid and the foreseen charging point's network were studied. Obtained values were 0.6-1.5 gCO(2eq)/km and 0.03-0.07 MJ(eq)/km for gasoline, 0.6-1.6 gCO(2eq)/km and 0.02-0.06 MJ(eq)/km for diesel, 3.7-8.5 gCO(2eq)/km and 0.06-0.17 MJ(eq)/km for EV. Monte Carlo technique was used for uncertainty analysis. We concluded that EV supply infrastructures are more carbon and energetic intensive. Contribution in overall vehicle LCA does not exceed 8%.